Using the Oilfield Investor Workbook, Richard and John identify the revenue impact on Schlumberger if oil prices were to reach $80 within the next two years. They also contrast and compare the impact of $80 oil on revenue growth for various market segments.
Based on their OMR (Oilfield Market Report) dataset, Richard and John identify the oilfield segments that have done the best (and worst!) job of achieving revenue growth per unit of volatility over the past decade.
Richard and John describe how the price impact of US shale oil production is similar to the discovery of Aztec gold and the development of the colonial tobacco industry. The guys also show how shale oil is currently saving consumers $80/bbl.
In this podcast, Richard and John discuss the implications of the recent decision by Norway’s sovereign wealth fund to sell its holdings in oil and gas exploration companies.
In this podcast, Richard and John discuss institutional investor actions that could support the creation of new, large scale ($10 billion market cap) oil service companies and identify three types of diversified enterprises that might emerge.
Although North American activity has increased 250% in the three years since the bottom of the oil price cycle, oil service firms have yet to see their profit margins recover to “normal” levels. Richard and John discuss the advantages of consolidation and the potential emergence of new “segment champions”.
Development and commercialization of innovative oilfield technology can require the close collaboration of an entrepreneur, a service firm, and an operator. In this episode, Richard and John identify six questions which successful collaborations must address.
In celebration of the 100th episode of the Drilldown, Richard and John review the genesis of the show and listenership trends, and discuss what’s next for the series and the company.
In this episode Richard and John share their tried and true 10 “rules of thumb” for tracking and estimating drilling and completion markets.
Knowing that an accurate forecast can be an important part of a good decision, we’re always interested in how to improve models by reducing uncertainty and bias. In this episode, John and Richard discuss sources of common modeling errors, such as recency bias, availability bias, and anchoring.